initial compatibility changes for upgrading multimer

environment.yml

@@ -3,6 +3,7 @@ channels:
   - conda-forge
   - bioconda
   - pytorch
+  - nvidia 
 dependencies:
   - python=3.9
   - libgcc=7.2
@@ -10,17 +11,16 @@ dependencies:
   - pip
   - openmm=7.7
   - pdbfixer
-  - cudatoolkit==11.3.*
-  - pytorch-lightning==1.5.10
+  - pytorch-lightning
   - biopython==1.79
-  - numpy==1.21
-  - pandas==2.0
+  - numpy
+  - pandas
   - PyYAML==5.4.1
   - requests
-  - scipy==1.7
+  - scipy
   - tqdm==4.62.2
-  - typing-extensions==3.10
-  - wandb==0.12.21
+  - typing-extensions
+  - wandb
   - modelcif==0.7
   - awscli
   - ml-collections
@@ -29,9 +29,10 @@ dependencies:
   - bioconda::hmmer==3.3.2
   - bioconda::hhsuite==3.3.0
   - bioconda::kalign2==2.04
-  - pytorch::pytorch=1.12.*
+  - pytorch::pytorch=2.1
+  - pytorch::pytorch-cuda=12.1
   - pip:
       - deepspeed==0.12.4
       - dm-tree==0.1.6
       - git+https://github.com/NVIDIA/dllogger.git
-      - git+https://github.com/Dao-AILab/flash-attention.git@5b838a8
+      - flash-attn 

openfold/data/data_pipeline.py

@@ -244,7 +244,7 @@ def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
     features["num_alignments"] = np.array(
         [num_alignments] * num_res, dtype=np.int32
     )
-    features["msa_species_identifiers"] = np.array(species_ids, dtype=np.object_)
+    features["msa_species_identifiers"] = np.array(species_ids, dtype=object)
     return features
 
 
@@ -590,7 +590,7 @@ def convert_monomer_features(
 ) -> FeatureDict:
     """Reshapes and modifies monomer features for multimer models."""
     converted = {}
-    converted['auth_chain_id'] = np.asarray(chain_id, dtype=np.object_)
+    converted['auth_chain_id'] = np.asarray(chain_id, dtype=object)
     unnecessary_leading_dim_feats = {
         'sequence', 'domain_name', 'num_alignments', 'seq_length'
     }
@@ -1296,7 +1296,7 @@ class DataPipelineMultimer:
         )
 
         mmcif_feats["release_date"] = np.array(
-            [mmcif_object.header["release_date"].encode("utf-8")], dtype=np.object_
+            [mmcif_object.header["release_date"].encode("utf-8")], dtype=object
         )
 
         mmcif_feats["is_distillation"] = np.array(0., dtype=np.float32)

openfold/model/primitives.py

@@ -28,7 +28,7 @@ if ds4s_is_installed:
 fa_is_installed = importlib.util.find_spec("flash_attn") is not None
 if fa_is_installed:
     from flash_attn.bert_padding import unpad_input
-    from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func
+    from flash_attn.flash_attn_interface import flash_attn_varlen_kvpacked_func
 
 import torch
 import torch.nn as nn
@@ -811,7 +811,7 @@ def _flash_attn(q, k, v, kv_mask):
     kv_unpad, _, kv_cu_seqlens, kv_max_s = unpad_input(kv, kv_mask)
     kv_unpad = kv_unpad.reshape(-1, *kv_shape[-3:])
    
-    out = flash_attn_unpadded_kvpacked_func(
+    out = flash_attn_varlen_kvpacked_func(
         q,
         kv_unpad,
         q_cu_seqlens,

setup.py

@@ -29,7 +29,7 @@ version_dependent_macros = [
 ]
 
 extra_cuda_flags = [
-    '-std=c++14',
+    '-std=c++17',
     '-maxrregcount=50',
     '-U__CUDA_NO_HALF_OPERATORS__',
     '-U__CUDA_NO_HALF_CONVERSIONS__',